Static two-dimensional (2D) or three-dimensional (3D) sub-surface imaging, applied to oil field exploration and monitoring, are numerous and have been explored since the late 1970's. In petroleum exploration applications of time-lapse subsurface imaging, thousands of stations have been incorporated and are large-scale. However, they are still based on centralized off-line processing and are typically accomplished by multiple active-source recordings where variations over multiple year spans are the main goal. In both industry and academia, the seismic exploration does not yet have the capability of illuminating the physical dynamics with high resolution and in real-time, as it involves collecting the raw seismic data from sensors to data loggers then manually retrieving data for post processing which may take months to complete.
Recent sensor network technology has matured to the point where it is now possible to deploy and maintain large networks for real-time geophysical monitoring. Also the computing and communication capability of each sensor can be utilized for distributed tomographic inversion. Seismic imaging algorithms commonly in use today cannot be directly implemented under field circumstances because they rely on centralized algorithms and require massive amounts of raw seismic data collected from sensors and transmitted to a central processing unit. However; real-time transmission of the raw seismic data is not feasible due to the severe bandwidth and energy limitations of low-power sensor networks. Time varying, real-time seismic tomography thus requires a new approach, both with respect to tomographic algorithms and sensor network design. The research challenge here is to develop a new method for processing raw seismic data and computing tomography in-situ in real-time, under the severe, restricting constraints of limited network resources (bandwidth, energy, computing power, memory, etc.).
Therefore, what is needed is systems and methods that overcome challenges in the art, some of which are described above.